remote-gpu-client/examples/inference_deep.py

import sys
import traceback
import os

print("🔮 Deep-NanoGPT Inference Script")

try:
    import torch
    import torch.nn as nn
    from torch.nn import functional as F
    import requests

    # --- Config (must match training) ---
    block_size = 256
    n_embd = 128
    n_head = 4
    n_layer = 72
    dropout = 0.1
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    
    # --- Storage ---
    storage_dir = "/home/user/app/storage/deep_experiment_v2"
    ckpt_path_a = os.path.join(storage_dir, 'ckpt_a.pt')
    ckpt_path_b = os.path.join(storage_dir, 'ckpt_b.pt')

    # --- Vocab (rebuild from data) ---
    url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt'
    data = requests.get(url).text
    chars = sorted(list(set(data)))
    vocab_size = len(chars)
    stoi = { ch:i for i,ch in enumerate(chars) }
    itos = { i:ch for i,ch in enumerate(chars) }
    encode = lambda s: [stoi.get(c, 0) for c in s]
    decode = lambda l: ''.join([itos[i] for i in l])

    # --- Model Classes ---
    class Head(nn.Module):
        def __init__(self, head_size):
            super().__init__()
            self.key = nn.Linear(n_embd, head_size, bias=False)
            self.query = nn.Linear(n_embd, head_size, bias=False)
            self.value = nn.Linear(n_embd, head_size, bias=False)
            self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
            self.dropout = nn.Dropout(dropout)

        def forward(self, x):
            B,T,C = x.shape
            k = self.key(x)   
            q = self.query(x) 
            wei = q @ k.transpose(-2, -1) * C**-0.5
            wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
            wei = F.softmax(wei, dim=-1)
            wei = self.dropout(wei)
            v = self.value(x)
            return wei @ v

    class MultiHeadAttention(nn.Module):
        def __init__(self, num_heads, head_size):
            super().__init__()
            self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
            self.proj = nn.Linear(n_embd, n_embd)
            self.dropout = nn.Dropout(dropout)

        def forward(self, x):
            out = torch.cat([h(x) for h in self.heads], dim=-1)
            return self.dropout(self.proj(out))

    class FeedForward(nn.Module):
        def __init__(self, n_embd):
            super().__init__()
            self.net = nn.Sequential(
                nn.Linear(n_embd, 4 * n_embd),
                nn.ReLU(),
                nn.Linear(4 * n_embd, n_embd),
                nn.Dropout(dropout),
            )
        def forward(self, x):
            return self.net(x)

    class BlockStandard(nn.Module):
        def __init__(self, n_embd, n_head):
            super().__init__()
            head_size = n_embd // n_head
            self.sa = MultiHeadAttention(n_head, head_size)
            self.ffwd = FeedForward(n_embd)
            self.ln1 = nn.LayerNorm(n_embd)
            self.ln2 = nn.LayerNorm(n_embd)

        def forward(self, x):
            x = x + self.sa(self.ln1(x))
            x = x + self.ffwd(self.ln2(x))
            return x

    class RMSNorm(nn.Module):
        def __init__(self, dim, eps=1e-6):
            super().__init__()
            self.eps = eps
            self.weight = nn.Parameter(torch.ones(dim))

        def _norm(self, x):
            return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

        def forward(self, x):
            return self._norm(x.float()).type_as(x) * self.weight

    class BlockMHC(nn.Module):
        def __init__(self, n_embd, n_head):
            super().__init__()
            head_size = n_embd // n_head
            self.sa = MultiHeadAttention(n_head, head_size)
            self.ffwd = FeedForward(n_embd)
            self.alpha1 = nn.Parameter(torch.tensor(0.9))
            self.beta1 = nn.Parameter(torch.tensor(0.1))
            self.ln1 = RMSNorm(n_embd)
            self.alpha2 = nn.Parameter(torch.tensor(0.9))
            self.beta2 = nn.Parameter(torch.tensor(0.1))
            self.ln2 = RMSNorm(n_embd)

        def forward(self, x):
            mix1 = self.alpha1 * x + self.beta1 * self.sa(x)
            x = self.ln1(mix1)
            mix2 = self.alpha2 * x + self.beta2 * self.ffwd(x)
            x = self.ln2(mix2)
            return x

    class GPT(nn.Module):
        def __init__(self, arch_type='standard'):
            super().__init__()
            self.arch_type = arch_type
            self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
            self.position_embedding_table = nn.Embedding(block_size, n_embd)
            if arch_type == 'standard':
                self.blocks = nn.Sequential(*[BlockStandard(n_embd, n_head) for _ in range(n_layer)])
                self.ln_f = nn.LayerNorm(n_embd)
            elif arch_type == 'mhc':
                self.blocks = nn.Sequential(*[BlockMHC(n_embd, n_head) for _ in range(n_layer)])
                self.ln_f = RMSNorm(n_embd)
            self.lm_head = nn.Linear(n_embd, vocab_size)

        def forward(self, idx, targets=None):
            B, T = idx.shape
            tok_emb = self.token_embedding_table(idx)
            pos_emb = self.position_embedding_table(torch.arange(T, device=device))
            x = tok_emb + pos_emb
            x = self.blocks(x)
            x = self.ln_f(x)
            logits = self.lm_head(x)
            return logits, None
            
        def generate(self, idx, max_new_tokens):
            for _ in range(max_new_tokens):
                idx_cond = idx[:, -block_size:]
                logits, _ = self(idx_cond)
                logits = logits[:, -1, :]
                probs = F.softmax(logits, dim=-1)
                idx_next = torch.multinomial(probs, num_samples=1)
                idx = torch.cat((idx, idx_next), dim=1)
            return idx

    # --- Load Models ---
    print(f"📦 Loading Model A (Standard) from {ckpt_path_a}...")
    model_a = GPT(arch_type='standard').to(device)
    model_a.load_state_dict(torch.load(ckpt_path_a, map_location=device))
    model_a.eval()

    print(f"📦 Loading Model B (mHC) from {ckpt_path_b}...")
    model_b = GPT(arch_type='mhc').to(device)
    model_b.load_state_dict(torch.load(ckpt_path_b, map_location=device))
    model_b.eval()

    # --- Inference ---
    PROMPT = "ROMEO:"  # Shakespearean prompt
    MAX_TOKENS = 300

    print(f"\n🎭 Prompt: '{PROMPT}'")
    print(f"🔢 Max Tokens: {MAX_TOKENS}")

    context = torch.tensor([encode(PROMPT)], dtype=torch.long, device=device)

    print("\n--- MODEL A (Standard GPT, 72 Layers) ---")
    with torch.no_grad():
        out_a = model_a.generate(context.clone(), max_new_tokens=MAX_TOKENS)
    print(decode(out_a[0].tolist()))

    print("\n--- MODEL B (mHC GPT, 72 Layers) ---")
    with torch.no_grad():
        out_b = model_b.generate(context.clone(), max_new_tokens=MAX_TOKENS)
    print(decode(out_b[0].tolist()))

    print("\n✅ Inference Complete.")

except Exception as e:
    print(f"\n❌ FATAL ERROR: {e}")
    traceback.print_exc()